{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image Browser"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example shows how to browse through a set of images with a slider."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from ipywidgets import interact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the digits dataset from [scikit-learn](http://scikit-learn.org/stable/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def browse_images(digits):\n",
    "    n = len(digits.images)\n",
    "    def view_image(i):\n",
    "        plt.imshow(digits.images[i], cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "        plt.title('Training: %s' % digits.target[i])\n",
    "        plt.show()\n",
    "    interact(view_image, i=(0,n-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/html": [
       "<img src= class=\"jupyter-widget\">\n",
       "<script type=\"application/vnd.jupyter-embedded-widgets\">[{},{\"layout\":\"IPY_MODEL_a29dd47519034214a873fc3aaa861a6c\",\"description\":\"i\",\"max\":1796,\"value\":1155},{},{\"children\":[\"IPY_MODEL_036aaec3e8524d04aad7f3a01ed2061f\"],\"layout\":\"IPY_MODEL_4b206c4b367d43fa806b0cea05c72808\",\"_dom_classes\":[\"widget-interact\"]}]</script>"
      ]
     },
     "metadata": {
      "isWidgetSnapshot": true
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f27f36c4dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "browse_images(digits)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3+"
  },
  "widgets": {
   "state": {
    "036aaec3e8524d04aad7f3a01ed2061f": {
     "views": []
    },
    "4b206c4b367d43fa806b0cea05c72808": {
     "views": []
    },
    "6e4efab090144cbfbdbeb6c6f93f14a6": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 6,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
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